目的:(1)评估住院后社会危险因素与计划外再入院和急诊护理的关系。(2)创建社会风险评分指标。
方法:我们分析了退伍军人事务部(VA)企业数据仓库的管理数据。设置为参加国家社会工作人员配备计划的VA医疗中心。
方法:我们将社会相关诊断分组,放映,评估,和程序代码分为九个社会风险领域。我们使用逻辑回归来检查领域在出院后30天内预测计划外再入院和急诊科(ED)使用的程度。协变量是年龄,性别,和医疗再入院风险评分。我们使用模型估计来创建一个百分位得分,表明退伍军人与健康相关的社会风险。
方法:我们纳入了156,690名退伍军人入院,从10月1日起出院回家,2016年9月30日,2022年。
结果:30天计划外再入院率为0.074,ED使用率为0.240。调整后,再入院概率最大的社会风险是粮食不安全(调整概率=0.091[95%置信区间:0.082,0.101]),法律需要(0.090[0.079,0.102]),和邻里剥夺(0.081[0.081,0.108]);与无社会风险(0.052)相比。ED使用的最大调整概率是那些经历过粮食不安全的人(调整概率0.28[0.26,0.30]),法律问题(0.28[0.26,0.30]),和暴力(0.27[0.25,0.29]),与无社会风险(0.21)相比。社会风险评分在第95百分位数的退伍军人的计划外护理率高于第95百分位数的退伍军人。VA中使用的临床预测工具。
结论:有社会风险的退伍军人住院后可能需要专门的干预措施和有针对性的资源。我们提出了一种评分方法来对社会风险进行评分,以用于临床实践和未来的研究。
OBJECTIVE: (1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index.
METHODS: We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program.
METHODS: We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans\' health-related social risk.
METHODS: We included 156,690 Veterans\' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022.
RESULTS: The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA.
CONCLUSIONS: Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.